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ORCIDEldar Kurtic
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2020 – today
- 2024
[j1]Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh:
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization. Trans. Mach. Learn. Res. 2024 (2024)- [c8]Eldar Kurtic, Amir Moeini, Dan Alistarh:
Mathador-LM: A Dynamic Benchmark for Mathematical Reasoning on Large Language Models. EMNLP 2024: 17020-17027 - [c7]Ionut-Vlad Modoranu, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, Dan Alistarh:
Error Feedback Can Accurately Compress Preconditioners. ICML 2024 - [i16]Abhinav Agarwalla, Abhay Gupta, Alexandre Marques, Shubhra Pandit, Michael Goin
, Eldar Kurtic, Kevin Leong, Tuan Nguyen, Mahmoud Salem, Dan Alistarh, Sean Lie, Mark Kurtz:
Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment. CoRR abs/2405.03594 (2024) - [i15]Ionut-Vlad Modoranu, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic, Thomas Robert, Peter Richtárik, Dan Alistarh:
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence. CoRR abs/2405.15593 (2024) - [i14]Eldar Kurtic, Amir Moeini, Dan Alistarh:
Mathador-LM: A Dynamic Benchmark for Mathematical Reasoning on Large Language Models. CoRR abs/2406.12572 (2024) - [i13]Armand Nicolicioiu, Eugenia Iofinova, Eldar Kurtic, Mahdi Nikdan, Andrei Panferov, Ilia Markov, Nir Shavit, Dan Alistarh:
Panza: A Personalized Text Writing Assistant via Data Playback and Local Fine-Tuning. CoRR abs/2407.10994 (2024) - 2023
- [c6]Alexandra Peste, Adrian Vladu, Eldar Kurtic, Christoph H. Lampert, Dan Alistarh:
CrAM: A Compression-Aware Minimizer. ICLR 2023 - [c5]Mahdi Nikdan, Tommaso Pegolotti, Eugenia Iofinova, Eldar Kurtic, Dan Alistarh:
SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge. ICML 2023: 26215-26227 - [c4]Eldar Kurtic, Elias Frantar, Dan Alistarh:
ZipLM: Inference-Aware Structured Pruning of Language Models. NeurIPS 2023 - [c3]Denis Kuznedelev, Eldar Kurtic, Elias Frantar, Dan Alistarh:
CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models. NeurIPS 2023 - [i12]Eldar Kurtic, Elias Frantar, Dan Alistarh:
ZipLM: Hardware-Aware Structured Pruning of Language Models. CoRR abs/2302.04089 (2023) - [i11]Mahdi Nikdan, Tommaso Pegolotti, Eugenia Iofinova, Eldar Kurtic, Dan Alistarh:
SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks. CoRR abs/2302.04852 (2023) - [i10]Denis Kuznedelev, Soroush Tabesh, Kimia Noorbakhsh, Elias Frantar, Sara Beery, Eldar Kurtic, Dan Alistarh:
Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression. CoRR abs/2303.14409 (2023) - [i9]Ionut-Vlad Modoranu, Aleksei Kalinov, Eldar Kurtic, Dan Alistarh:
Error Feedback Can Accurately Compress Preconditioners. CoRR abs/2306.06098 (2023) - [i8]Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh:
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization. CoRR abs/2308.02060 (2023) - [i7]Eldar Kurtic, Denis Kuznedelev, Elias Frantar, Michael Goin, Dan Alistarh:
Sparse Fine-tuning for Inference Acceleration of Large Language Models. CoRR abs/2310.06927 (2023) - [i6]Eldar Kurtic, Torsten Hoefler, Dan Alistarh:
How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry" Benchmark. CoRR abs/2312.13547 (2023) - 2022
- [c2]Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, Dan Alistarh:
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models. EMNLP 2022: 4163-4181 - [i5]Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, Dan Alistarh:
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models. CoRR abs/2203.07259 (2022) - [i4]Zlatan Ajanovic, Emina Alickovic, Aida Brankovic, Sead Delalic, Eldar Kurtic, Salem Malikic, Adnan Mehonic, Hamza Merzic, Kenan Sehic, Bahrudin Trbalic:
Vision for Bosnia and Herzegovina in Artificial Intelligence Age: Global Trends, Potential Opportunities, Selected Use-cases and Realistic Goals. CoRR abs/2209.03990 (2022) - [i3]Eldar Kurtic, Dan Alistarh:
GMP*: Well-Tuned Global Magnitude Pruning Can Outperform Most BERT-Pruning Methods. CoRR abs/2210.06384 (2022) - [i2]Denis Kuznedelev, Eldar Kurtic, Elias Frantar, Dan Alistarh:
oViT: An Accurate Second-Order Pruning Framework for Vision Transformers. CoRR abs/2210.09223 (2022) - 2021
- [c1]Elias Frantar, Eldar Kurtic, Dan Alistarh:
M-FAC: Efficient Matrix-Free Approximations of Second-Order Information. NeurIPS 2021: 14873-14886 - [i1]Elias Frantar, Eldar Kurtic, Dan Alistarh:
Efficient Matrix-Free Approximations of Second-Order Information, with Applications to Pruning and Optimization. CoRR abs/2107.03356 (2021)
Coauthor Index
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